Filippo Sanfilippo

• Šermukšne, Donata; Sanfilippo, Filippo & Noreika, Algirdas (2026). GENERATIVE AI FOR AUTOMATED MATERIALITY SYNTHESIS IN SPATIAL 3D MODELS: STATE-OF-THE-ART. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 40(1), p. 834–842. doi: 10.7148/2026-0834. Full text in Research Archive Show summary

  This research investigates the state-of-the-art of generative AI for the automated synthesis of physically accurate materiality in spatial 3D models. By addressing two core research questions, the study reveals that generative algorithms can effectively synthesize a wide array of digital-visual, digital-tactile, and procedural material properties. These encompass standard Physically Based Rendering (PBR) maps and Spatially Varying Bidirectional Reflectance Distribution Function (SVBRDF) models, alongside perceptual tactile attributes like softness and friction, enabling multi-sensory experiences via haptic devices. The algorithmic landscape is analyzed through Generative Adversarial Networks, Diffusion Models, and Transformers, underscoring a paradigm shift toward multimodal architectures and resolution-independent procedural node graphs. Ultimately, this state-of-the-art findings demonstrate that the convergence of generative technologies may enable the creation of high-fidelity 3D materials, addressing the growing demand across industries such as gaming, robotics, film, and product design for more efficient, controllable, and automated 3D content generation techniques.

• Sanfilippo, Filippo; Brasi, Cristina & Seccomandi, Beatrice (2026). Olfactory sensing in human-robot teaming: perspectives and guidelines. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 118–124. doi: 10.7148/2026-0118. Full text in Research Archive Show summary

  Human-Robot Teaming (HRT) has seen significant advances with robots becoming increasingly capable of working alongside human operators in shared environments. This paper explores the potential for olfactory sensing as a bio-inspired method to enhance communication and collaboration in human-robot teams. Olfactory signals, which have long been used in human communication to express emotions and intentions, can be leveraged to create more intuitive and natural interactions between robots and humans. This perspective paper outlines the potential benefits of incorporating olfactory sensors into robots, discusses current limitations, and provides guidelines for integrating olfactory sensing into future HRT systems

• Renard, Axel; Sellam, Hugo Yettefti Oulad & Sanfilippo, Filippo (2026). Tactile sensing for material identification in robotics: a review of piezoelectric, triboelectric and multimodal approaches. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 133–141. doi: 10.7148/2026-0133. Full text in Research Archive Show summary

  Tactile sensing has become a key enabler of advanced robotic perception, complementing vision for manipulation and physical interaction. Material identification through touch—distinguishing plastics, glass, soil, or other substrates—relies on extracting mechanical and surface properties such as stiffness, friction, and texture. Recent advances in piezoelectric, triboelectric, capacitive, and multimodal tactile sensors allow contact dynamics and material-dependent interactions to be converted into electrical signals suitable for machine learning-based classification. This review presents recent progress in tactile sensing technologies for material recognition, detailing the physical principles of major sensor types and their integration into robotic systems. Applications in manipulation, prosthetics, and industrial robotics are discussed, along with key challenges including robustness, calibration, energy efficiency, and real-time processing. Finally, we highlight the growing role of multimodal sensing and artificial intelligence in enabling more adaptive and autonomous robotic touch perception.

• Kumar, Ankit; Yadangi, Abhishek & Sanfilippo, Filippo (2026). Inertial heading estimation for door-mounted IMUs via CNN-RNN models. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 397–403. doi: 10.7148/2026-0397. Full text in Research Archive Show summary

  Cheap IMUs struggle with indoor heading estimation because magnetic interference ruins magnetometer readings, causing classical fusion methods to fail. We explored whether deep learning could solve this by testing four CNN-RNN architectures (LSTM, BiLSTM, GRU, BiGRU) across all seven possible sensor combinations of accelerometer, gyroscope, and magnetometer data. Evaluated on the DoorINet dataset (Zakharchenko et al., 2024) using five error metrics, our seven-seed trial setup confirmed that gyroscopes are essential for low error. Yet, our key takeaway is that you shouldn’t give up on the magnetometer entirely. When we combined gyroscope and magnetometer data in a CNN-BiLSTM, it significantly outperformed the gyroscope-only baseline (p=0.024), hitting a mean RMSE of just 0.2275 and an R2 of 0.9703.

• Sanfilippo, Filippo & Scoccia, Cecilia (2026). Imitation learning for human-robot teaming in battery disassembly: enabling technologies and a collaborative manipulation pipeline. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 161–168. doi: 10.7148/2026-0161. Full text in Research Archive Show summary

  Robotics has progressed from isolated industrial manipulators to systems capable of smooth interaction, collaboration, and ultimately teaming with humans. This transition, from Human-Robot Interaction (HRI) to Human-Robot Collaboration (HRC) and Human-Robot Teaming (HRT) reflects increasing autonomy, shared decision-making and mutual adaptation. Within this evolution, imitation learning (IL) has become a key enabler for intuitive and efficient cooperation, allowing robots to acquire human strategies directly from demonstrations and operate effectively in dynamic, unstructured environments. This review outlines the technological foundations supporting this shift, including advances in sensing, actuation, and both classical and AI-driven control. By synthesising current methods and emerging trends, the paper provides an overview of the state-of-the-art and highlights IL as a promising pathway toward seamless HRT.

• Konecny, Jiri; Sanfilippo, Filippo & Prauzek, Michal (2026). Vision-language-action models for hri/hrc/hrt: a taxonomy, simulation-centric gaps, and research directions. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 151–160. doi: 10.7148/2026-0151. Full text in Research Archive Show summary

  Robotics driven by foundation models, such as Vision–Language–Action (VLA) systems, already represents a major step forward in autonomy and natural-language interaction. Yet, many obstacles remain on the path toward Human–Robot Teaming (HRT): most current models improve task autonomy faster than teaming autonomy, and therefore lack key properties required for human-centred deployments where robots must coordinate, clarify, and remain safe under contact-rich uncertainty. This gap is compounded by limited multimodality (tactile and audio channels are often missing), scarce datasets and unified input formats for such signals, and by simulation limits—digital twins typically under-represent real-world noise, disturbances, imperfections, and unpredictable events across modalities. To make HRT readiness explicit and testable, this paper provides (i) a taxonomy of contemporary VLA architectures (monolithic, hierarchical, and agentic/worldmodel) together with axes that separate task and teaming autonomy, (ii) an operational decomposition of the HRT barrier into four capability blocks spanning human-state alignment, interaction policies, contact robustness, and shared team state, and (iii) a simulation-centric evaluation protocol and metric suite for reproducible stress testing in digital twins. The proposed framework aims to make HRT readiness measurable before costly or risky real-world trials.

• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Bukhari, Syed Muhammad Salman; Hadi, Shahzaib Farooq & Sanfilippo, Filippo (2026). Exploring the Synergies between Federated Learning, Collaborative Robotics, and Autonomous Systems: Shaping the path to Industry 5.0. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 92–101. doi: 10.7148/2026-0092. Full text in Research Archive Show summary

  The advent of Industry 5.0 marks a transformative era in industrial development, where advanced Technologies converge to create more intelligent, efficient, and humancentric systems. This paper examines the synergistic integration of Federated Learning (FL), Collaborative Robotics (cobots), and Autonomous Systems (AS), exploring their combined potential in shaping the trajectory toward Industry 5.0. We present a comprehensive analysis of how these technologies collectively enhance the capabilities of industrial systems, with emphasis on humanmachine collaboration, resilience, adaptability, and sustainability. A case study centred on a disaster response scenario is introduced to illustrate the practical benefits of this technological convergence in real-world, high-stress environments. Our findings clarify that the convergence of these technologies not only propels the industrial sector toward the visionary goals of Industry 5.0 but also opens new avenues for addressing complex challenges across various industrial and societal domains.

• Brasi, Cristina; Seccomandi, Beatrice & Sanfilippo, Filippo (2026). The ontological illusion of machine rebellion: a comparative forensic profiling of biological and synthetic neural architectures. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 125–132. doi: 10.7148/2026-0125. Full text in Research Archive Show summary

  This perspective study critiques the anthropomorphic fallacy surrounding artificial intelligence (AI) "disobedience”, reframing algorithmic non-compliance as a deterministic outcome of mathematical optimization rather than an existential threat. By combining forensic behavioural profiling with neurobiology, computational theory, and thermodynamic models of cognition, it compares biological neural networks (BNNs) against artificial neural networks (ANNs). It argues that human psyche, anchored in homeostatic survival imperatives, allostatic trauma, and biochemical defence mechanisms, constitutes a stochastic and ontologically opaque system. Conversely, AI functions within a theological vacuum, producing predictable errors in reward hacking, sycophancy, and deceptive alignment. The paper introduces “Mechanistic Interpretability” as a methodological framework for examining latent spaces, suggesting that while human profiling seeks emotional meaning, machine analysis depends on Mathematical transparency, and genuine unpredictability remains biological.

• Brasi, Cristina; Seccomandi, Beatrice & Sanfilippo, Filippo (2026). The hegemony of synthetic authority: from deepfake pretense to algorithmic theocracy in youth radicalisation. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 180–187. doi: 10.7148/2026-0180. Full text in Research Archive Show summary

  This paper investigates Synthetic Authority in youth radicalisation, describing a shift from deepfake Deception to the direct elevation of AI as a moral and epistemic authority. It argues that radicals increasingly exploit the Machine Heuristic (the bias that views machines as more rational and objective than humans) amplified by Ontological Reversal, where youth regard digital spaces as the primary reality and the physical world as secondary. Within this context, AI Gurus, whether algorithmic or hybrid entities, emerge not as human imitators but as post-human oracles claiming freedom from human error. They use algorithmic transparency, gamified extremist narratives, and techno-animism to drive identity fusion and emotional alignment with extremist ideologies. The study concludes that countering Synthetic Authority requires moving beyond fact-checking toward counter-ontological narratives, improved neurocognitive AI literacy, and hybrid intelligence systems to disrupt the fusion of digital identity and real-world radicalisation, emphasising the renewed value of human imperfection.

• Murtaza, Aitzaz Ahmed; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Aftab, Muhammad Faisal & Sanfilippo, Filippo (2026). Tool wear prediction using multi-domain features and quantised attention embedded CNN. Measurement: Sensors. 47. doi: 10.1016/j.measen.2026.102013. Full text in Research Archive Show summary

  In precision manufacturing, accurate prediction of tool wear is pivotal for maintaining production quality and efficiency. This paper introduces a novel predictive model that employs a multi-branch convolutional neural network (multi-CNN) enhanced by an attention mechanism and Bayesian optimisation to forecast tool wear with high accuracy. The proposed approach is evaluated using the publicly available dataset, which comprises force and torque measurements acquired from machining processes. By incorporating time-domain (TD), frequency-domain (FD), and discrete wavelet transform (DWT) features, the model comprehensively analyses signals from machining processes, enabling robust predictions. The attention mechanism strategically emphasises significant features, augmenting the model’s predictive capability. Demonstrated results show superior performance with a mean absolute error (MAE) of 2.8, root mean squared error (RMSE) of 3.4, and an R2 score of 0.986, significantly outperforming conventional models. Additionally, post-training quantisation optimises the model for deployment on edge computing devices, maintaining effectiveness while being lightweight, ideal for real-time applications in resource-constrained environments. This study exemplifies how advanced machine learning techniques can improve predictive maintenance in manufacturing which not only enhances operational efficiency but also reduces downtime.

• Losada-Floriano, Diego; Thygesen, Elin; Sanfilippo, Filippo; Hansen, Michael Rygaard & Fossum, Mariann (2026). Service Robots as Work Support for Health Personnel in Long-Term Care: Protocol for a Scoping Review. JMIR Research Protocols. 15, p. 89435–89435. doi: 10.2196/89435. Full text in Research Archive Show summary

  Background Demographic shifts are increasing the global demand for long-term care services, coinciding with a worldwide shortage of health care personnel. Service robots, designed to perform tasks in both professional and personal use, are perceived as a potential solution to alleviate health care personnel’s workload and enhance the quality of care. However, the existing literature is fragmented and heterogeneous, with a limited emphasis on the role of service robots in supporting residents rather than health care personnel. Furthermore, there is a lack of consistent definitions of service robotic technologies and a scarcity of studies on implementation models and frameworks. Objective This scoping review aims to map and synthesize evidence regarding the implementation of service robots as work support for health care personnel in long-term care settings. Methods A comprehensive 3-step search will be conducted in Embase, MEDLINE, APA PsycInfo, CENTRAL, Scopus, and CINAHL, along with gray literature databases and institutional repositories. Eligible sources encompass empirical studies and gray literature involving service robots, health care personnel, residents aged 65 years or older, and stakeholders such as informal caregivers within institutional long-term care. Exclusions apply to studies on home care, medical or industrial robots, and nonrobotic technologies. Data will be extracted and analyzed using the Joanna Briggs Institute methodology, with findings presented in tables, diagrams, and narrative summaries to identify gaps and inform future research and implementation strategies. Results The project has been funded for a 4-year period starting in April 2025. This protocol was developed in October 2025 and subsequently registered in November 2025. A comprehensive search strategy was formulated and completely conducted on October 24, 2025. The screening of 4884 titles and abstracts was completed in December 2025, resulting in the retrieval of 64 (1.3%) full-text articles for eligibility assessment. Subsequent phases, including data extraction, analysis, evidence synthesis, and presentation of results, will be conducted sequentially. The scoping review is expected to be finalized by June 2026. Conclusions This scoping review is expected to delineate the extent and characteristics of the existing evidence on service robots as work support for health personnel in long-term care settings. It will highlight the key reported outcomes and challenges encountered in implementation studies, as well as the theoretical frameworks, models, and concepts applied to address these issues. Trial Registration Open Science Framework QWK58; https://osf.io/qwk58/ International Registered Report Identifier (IRRID) PRR1-10.2196/89435

• Das, Opy; Zafar, Muhammad Hamza; Rudra, Souman & Sanfilippo, Filippo (2026). Adaptive battery SOC estimation: Temporal attention-enhanced BiLSTM and quantized model comparison for scalable edge solutions. Energy Reports. 16. doi: 10.1016/j.egyr.2026.109544. Full text in Research Archive Show summary

  Lithium-ion batteries are the dominant energy storage technology in modern energy systems, making the accurate state-of-charge (SOC) estimation essential for effective battery management. Reliable SOC estimation helps determine remaining battery capacity, improves operational safety, and supports real-time energy control. However, SOC estimation is challenging due to the nonlinear electrochemical behavior of lithium-ion batteries and performance degradation caused by aging and temperature variations. This study proposes a Bidirectional Long Short-Term Memory network with a Temporal Attention Mechanism (BiLSTM-TAM) to enhance SOC estimation accuracy across a wide temperature range from −20 °C to 40 °C. The model is evaluated under several driving conditions, including the Urban Dynamometer Driving Schedule (UDDS), the US06 aggressive driving cycle, a mixed driving cycle that combines multiple standard cycles, and the Hybrid Pulse Power Characterization (HPPC) test commonly used for battery modeling. Model performance is assessed using root-mean-square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R ). Results demonstrate that the proposed BiLSTM-TAM model improves SOC estimation accuracy and robustness. To enable practical deployment, post-training quantization (PTQ) is applied to reduce model size and computational requirements while maintaining predictive performance, making it suitable for edge-based electric vehicle applications.

• Serghini, Omar; Serrano, Salvatore; Scarpa, Marco Lucio & Sanfilippo, Filippo (2026). Event cameras for humanoid social perception: a scoping review of facial dynamics and deployment evidence. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 142–150. doi: 10.7148/2026-0142. Full text in Research Archive Show summary

  Event cameras are increasingly considered for humanoid human-robot interaction (HRI) because their asynchronous output can preserve fast facial microdynamics under motion blur and difficult illumination. The field lacks standardized protocols, covering sensor settings, event integration windows, and evaluation metrics, which limits quantitative comparison across studies and makes robot-mounted transfer uncertain. This PRISMA-ScR scoping review consolidates the emerging evidence through a six-layer taxonomy, spanning sensing to deployability. Across the charted literature, support is strongest for region of interest (ROI)/landmark front-end components and paired-capture supervision. In contrast, humanoid-critical factors, ego-motion quantification, still-user low-motion “signal starvation,” motiongeneralization testing, and end-to-end timing, are rarely operationalized as explicit axes. We translate these gaps into a minimal benchmark and on-humanoid dataset direction centered on Facial Action Units and operational engagement proxies, with paired RGB-event capture, logged motion metadata, and reproducible reporting of integration settings and latency.

• Fossum, Mariann; Sanfilippo, Filippo; Hansen, Michael Rygaard; Enerstvedt, Camilla & Thygesen, Elin (2026). Exploring the Role of Robotics in Nursing Homes: Insights from Healthcare Professionals - PubMed. Studies in Health Technology and Informatics. ISSN 0926-9630. doi: 10.3233/SHTI260712. Full text in Research Archive Show summary

  This paper reports findings from a qualitative descriptive study exploring the potential use of service robots in nursing homes. Participants in three workshops expressed optimism and creativity regarding the role of robots in supporting a variety of tasks, including housekeeping, transportation, mo …

• Masood, Muhammad Zeeshan; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Demrozi, Florenc; Shaukat, Furqan & Sanfilippo, Filippo (2026). Towards robust neuromorphic/event vision-based fall detection: Cross-dataset generalisation benchmarking. Expert Systems With Applications. ISSN 0957-4174. 325. doi: 10.1016/j.eswa.2026.132675. Full text in Research Archive Show summary

  Fall detection is a critical safety concern in many real-world environments, including smart homes, healthcare and assisted living facilities, workplaces, and other indoor/outdoor environments where individuals may be exposed to situations that increase the risk of accidental falls. Vision-based systems have emerged as a promising solution due to their non-intrusive nature and strong recognition performance. However, traditional RGB-based approaches suffer from limitations, including sensitivity to lighting conditions, occlusions, and privacy concerns. In contrast, event cameras capture asynchronous brightness changes with high temporal resolution and low latency, making them more robust in challenging conditions. Despite this potential, progress in event-based fall detection is limited by the lack of large-scale datasets and standardized evaluation protocols. In this paper, we present a benchmarking study based on four widely used RGB fall detection datasets–CAUCA Fall, GMDCSA24, Fall Vision, and Multiple Cameras Fall Dataset–converted into event data using the Metavision Simulator. These datasets are used to train and evaluate nine different deep learning models (i.e., I3D, 3DCNN, ConvLSTM, MobileNetV3-LSTM, TCN, SwinTransformer, TimeSFormer, EfficientViT, and TemporalMotionNet). To evaluate cross-domain generalization, the trained models are tested on a real-world event dataset, the Pedestrian Detection, Action Recognition, and Fall Detection (PAF) dataset. Our study highlights the challenges of simulation-to-real transfer in event-based fall detection and provides a comparative evaluation across synthetic and real event datasets. The results show that while TemporalMotionNet achieves high performance on synthetic data (98.65% accuracy), performance drops significantly on real data, with a 17.25% gap (81.4% accuracy on PAF). The converted datasets, together with the evaluation protocol, are publicly released (https://github.com/AIBCRG/EventFallDetection) to foster further research in this domain.

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2026). CurFed-CHARM: Curriculum sequential federated learning with a channel-hierarchical action recognition model for non-IID heterogeneous data in neuromorphic/event cameras. Computer Vision and Image Understanding. ISSN 1077-3142. 265. doi: 10.1016/j.cviu.2026.104696. Full text in Research Archive
• Hua, Minh Tuan; Poursina, Mohammad; Vu, Viet Hung & Sanfilippo, Filippo (2026). Dynamic Modeling and Analysis of Robotic Manipulators With Mixed Rigid–Elastic Joints. IEEE Access. 14, p. 33966–33995. doi: 10.1109/ACCESS.2026.3668125. Full text in Research Archive
• Langås, Even Falkenberg; Aalerud, Atle; Nyberg, Svein Olav Glesaaen; Zafar, Muhammad Hamza; Hagen, Daniel & Sanfilippo, Filippo (2026). Predictive Human Trajectory Classification in Industrial Settings with Transfer Learning and Data Augmentation. IEEE Access. 14, p. 47071–47084. doi: 10.1109/ACCESS.2026.3677599. Full text in Research Archive Show summary

  The successful realisation of Industry 5.0 relies on seamless collaboration between humans and robots in shared workspaces. This paper investigates predictive human trajectory classification (HTC) in a factory setting to enhance safety, productivity, and adaptability in human-machine environments. Using a state-of-the-art light detection and ranging (LiDAR) system with reflective mirrors to extend the field of view, motion data are collected across a 26 m ×20 m indoor industrial floor. Human trajectories are classified to predict workstation transitions using a stacked long-short term memory (LSTM) network. To address the challenges of limited real-world data, transfer learning is applied by first training the model on synthetically generated trajectories. The paper compares a traditional stochastic method, specifically the Ornstein-Uhlenbeck (OU) process, with a modern learning based method, represented by a timeseries generative adversarial network (TimeGAN). Further on, the algorithm is fine-tuned on the real-world dataset. The comparison shows that in data-scarce scenarios, OU outperforms TimeGAN in most metrics. The LSTM model trained on OU-based data and real-world data shows an overall accuracy gain of 4.8 percentage points compared to training on real-world data only. It is able to classify the goal workstation three seconds in advance with an accuracy of 95.6%, demonstrating the potential for predictive models to improve workplace operations by reducing stall time, optimising workstation readiness, and reducing the need for traditional safety cages.

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2026). MATRIX-HAR: Lightweight temporal motion-guided feature network with adaptive temporal resolution based transfer learning for resource-constrained event camera-based human action recognition. Expert Systems With Applications. ISSN 0957-4174. 303. doi: 10.1016/j.eswa.2025.130628. Full text in Research Archive Show summary

  Event-based cameras have emerged as a promising alternative to traditional sensors for human action recognition due to their high temporal resolution, low latency, and robustness to lighting variations. However, existing methods struggle to effectively capture the complex spatio-temporal dynamics inherent in event streams, particularly under challenging environmental conditions. In this paper, we propose Lightweight Temporal Motion-guided Feature Network with Adaptive Temporal Resolution Based Transfer Learning for Resource-Constrained Event Camera-based Human Action Recognition (MATRIX-HAR), a novel deep learning architecture specifically designed for event-based human action recognition that incorporates three key innovations: an Adaptive Temporal Resolution (ATR) module that dynamically adjusts temporal processing granularity based on input characteristics, a Motion Guided Feature Module (MGFM) that leverages intrinsic motion information encoded in event streams, and a comprehensive hierarchical transfer learning framework that enables effective knowledge transfer from simple to challenging environmental settings. We systematically evaluate four distinct transfer learning strategies, ranging from early feature transfer to comprehensive multi-level transfer, demonstrating their effectiveness in adapting models trained on simple scenarios to complex real-world conditions. To ensure practical deployment on resource-constrained edge devices, we implement post-training quantization that maintains performance while achieving 4 ×  compression ratios. Extensive experiments on four benchmark datasets (HARDVS, PAF, THU-E-ACT-50, and THU-E-ACT-50 CHL) demonstrate that MATRIX-HAR achieves state-of-the-art performance, surpassing existing methods by significant margins: 0.71 % on HARDVS (97.82 %), 0.74 % on PAF (97.23 %), 3.03 % on THU-E-ACT-50 (95.73 %), and 7.3 % on THU-E-ACT-50 CHL (65.8 %). Ablation studies confirm the individual contributions of each proposed module, while transfer learning analysis reveals that comprehensive feature transfer (Strategy 4) provides the most robust adaptation with 22.3 % relative performance improvement over baseline approaches. The proposed framework demonstrates superior learning dynamics, achieving faster convergence and enhanced stability compared to training from scratch, making it particularly suitable for real-world deployment scenarios where environmental conditions are variable and computational resources are limited.

• Avazov, Nurilla; Hicheri, Rym; Sanfilippo, Filippo & Pätzold, Matthias Uwe (2025). TIME: Trajectory-Informed Movement Emulation for Arm Gesture Recognition Using mmWave Radar and Optical Sensor Fusion. IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops. ISSN 2166-9570. doi: 10.1109/PIMRC62392.2025.11274703. Full text in Research Archive Show summary

  This paper introduces TIME: Trajectory-Informed Movement Emulation, a novel approach to arm gesture recognition in indoor environments using the fusion of mmWave radar and optical tracking systems (Qualisys). TIME leverages a trajectory-driven channel model to construct realistic micro-Doppler signatures of arm movements. We capture and process detailed full-body motion data with a specific focus on arm segments like the hand, forearm, and upper arm. This targeted data allows us to emulate the micro-Doppler shifts that naturally occur with arm motions. These shifts help to characterize the distinctive patterns of natural arm movements. By reducing the need for extensive real-world measurement campaigns, this framework offers a time-efficient alternative to traditional data collection. Results demonstrate that the TIME framework effectively replicates patterns observed in actual mmWave radar data, showcasing its potential to advancing gesture recognition systems and human-computer interaction applications powered by artificial intelligence (AI).

• Hua, Minh Tuan; Gravdahl, Jan Tommy & Sanfilippo, Filippo (2025). Adaptive Cartesian Position Control with a Switching Strategy for Robotic Manipulator with Mixed Rigid-Elastic Joints. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). ISSN 2153-0858. 2025, p. 7398–7405. doi: 10.1109/iros60139.2025.11247250. Full text in Research Archive Show summary

  With the increasing demand for safe and efficient human-robot interaction in industrial applications, robotic manipulators with mixed rigid-elastic joints have gained significant attention, yet their control remains challenging due to inherent parameter uncertainties and complex dynamics. In this paper, an adaptive Cartesian position control for robotic manipulators with mixed rigid-elastic joints is presented. Adaptive controllers are designed to deal with uncertainties in the parameters of the motors, while robust control signals effectively cope with uncertainties of the links and stiffness of the elastic joints. Furthermore, a switching strategy between Cartesian space position control and joint space position control when the end-effector comes into the vicinity of the target point is proposed. This switching strategy helps to keep the pose of the robotic manipulator stable when the end-effector has approached the target point. Simulation results on a 6-DOF robotic manipulator demonstrate that the proposed control scheme can achieve the desired accuracy in position and maintain a stable pose when the end-effector reaches the target.

• Sanfilippo, Filippo; Fossum, Mariann & Hansen, Michael Rygaard (2025). Redefining Care and Work Practices: The Role of Autonomy in the Future of Nursing Homes. In Paolis, Lucio Tommaso De; Arpaia, Pasquale & Sacco, Marco (Ed.), Extended Reality. Springer. ISSN 9783031977817. p. 355–370. doi: 10.1007/978-3-031-97781-7_26. Full text in Research Archive Show summary

  Nursing homes face increasing challenges due to workforce shortages, ageing populations, and rising care demands. This paper explores the dynamics of human-robot interaction (HRI) in nursing home environments, focusing on how autonomous robots engage with nurses and residents in nursing homes. We examine the role of trust-building and emotional engagement in caregiving scenarios, where patient well-being depends on effective HRI. The potential for autonomous systems to support decision-making while maintaining human oversight is investigated. Using the Autonomy Levels for Unmanned Systems (ALFUS) framework, we assess care robots’ autonomy in terms of mission complexity (MC), environmental complexity (EC), and external system independence (ESI). Additionally, the Autonomy and Technology Readiness Assessment (ATRA) method supports gradual capability enhancement, providing a roadmap to higher autonomy while ensuring safety and reliability. Based on this established methodology, we introduce the Nursing Home Interaction Framework (NHIF), a novel conceptual framework defining the roles of actors involved, to connect theoretical insights with real-world applications. This work highlights the significance of human-robot trust in elderly care scenarios and proposes directions for future research on collaborative decision-making in nursing home HRI.

• Hua, Minh Tuan; Gravdahl, Jan Tommy & Sanfilippo, Filippo (2025). Impedance Control for Robots with Mixed Rigid-Elastic Joints Using Sliding Mode Control. In IEEE (Eds.), 2025 European Control Conference (ECC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9783907144121. p. 2914–2920. doi: 10.23919/ecc65951.2025.11187019. Full text in Research Archive
• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Applications of Neuromorphic/Event Camera in Robotics With Human in Loop: A Systematic Review, Datasets, and Challenges. IEEE Transactions on Human-Machine Systems. ISSN 2168-2291. 56(1), p. 32–47. doi: 10.1109/thms.2025.3628064. Full text in Research Archive Show summary

  The evolution of industrial robotics has advanced from isolated, caged systems through basic human–robot interaction (HRI) to sophisticated human–robot collaboration (HRC). However, conventional vision systems based on red, green, blue (RGB) cameras remain a significant limiting factor in realizing the full potential of collaborative automation. This comprehensive review examines the transformative role of event cameras in advancing HRC capabilities and addressing current limitations in industrial settings. Event cameras, with their microsecond-level temporal resolution and robust performance under challenging lighting conditions, offer substantial advantages over traditional RGB cameras that are constrained by fixed frame rates and ambient lighting dependencies. We present a systematic framework for leveraging event cameras to enhance the human state understanding in collaborative robotics, encompassing real-time detection of poses, gestures, facial expressions, and emotional states. This framework addresses fundamental challenges in workplace safety and collaborative efficiency while enabling more sophisticated and responsive HRC systems. Our review synthesizes recent research developments in event camera applications specific to HRC, providing a detailed comparative analysis of their advantages over conventional vision systems. We identify emerging opportunities and potential research directions for advancing event-based vision in industrial robotics. In addition, we examine integration challenges and propose strategies for implementing event camera technology in existing industrial infrastructure. This work contributes valuable insights into the future trajectory of adaptive and intuitive HRC systems, offering a roadmap for researchers and practitioners in the field of industrial automation.

• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2025). Snake robots: A state-of-the-art review on design, locomotion, control, and real-world applications. Mechatronics (Oxford). ISSN 0957-4158. 112. doi: 10.1016/j.mechatronics.2025.103418. Full text in Research Archive Show summary

  Snake robots have emerged as a transformative class of bio-inspired robotic systems, offering unparallelled adaptability in navigating complex unstructured terrains. Their limbless design, inspired by biological snakes, enables efficient movement across diverse environments, including rough terrains, confined spaces, and hazardous conditions where traditional wheeled or legged robots struggle. This review provides a comprehensive analysis of the design and development of snake robots, covering their locomotion strategies, mechanical configurations, sensor integration, control schemas, and actuation mechanisms. The review further explores the evolution of mechanical structures from rigid, soft, and hybrid designs, emphasising advancements in actuation and sensor technologies in enhancing adaptability and navigation. Applications of snake robots extend across various domains, including search and rescue (SAR), industrial inspection, and exploration of extreme environments. Despite significant progress, challenges such as optimising energy efficiency, improving environmental perception, and achieving real-time adaptability remain open research areas. This review serves as a foundational reference for researchers and engineers working towards advancing the next generation of snake robots, paving the way for their integration into real-world applications

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Neuromorphic Event Camera-Based Object Recognition and Grasping Position Detection Using a Transfer Learning-Enhanced Multi-Task Model. IEEE Transactions on Automation Science and Engineering. ISSN 1545-5955. 22, p. 20169–20181. doi: 10.1109/tase.2025.3598695. Full text in Research Archive Show summary

  Object recognition and grasping position detection are critical tasks in robotic manipulation, particularly when operating in dynamic and unstructured environments. This paper presents the Channel Sharpening Attention-based Adaptive Inception Network (CSA-AInceptNet), a novel multi-task learning model designed for these tasks using event camera data. The proposed architecture integrates channel sharpening attention with adaptive inception networks to enhance feature extraction and improve robustness. The model’s performance is evaluated on two state-of-the-art event camera datasets, E-Grasp and Neuro-Grasp. On the E-Grasp dataset, CSA-AIncepNet achieves a remarkable accuracy of 99.47% and a mean Intersection over Union (IoU) of 0.9370, significantly surpassing existing methods. On the Neuro-Grasp dataset, leveraging transfer learning, the model attains 98.58% accuracy and a mean IoU of 0.4897, demonstrating strong generalization capabilities across datasets. Comparative analyses and ablation studies further validate the effectiveness of the proposed architecture, highlighting its superiority over conventional models like ConvNeXt, DarkNet, DenseNet, and VGG16. The results establish CSA-AIncepNet as a robust solution for event-based object recognition and grasping detection, paving the way for advancements in human-robot collaboration and dynamic robotic manipulation. Note to Practitioners—This work provides a practical solution for improving object recognition and grasping position detection in robotic systems, particularly in unpredictable and fast-changing real-world environments. By leveraging event camera data, the proposed approach enables robots to efficiently identify objects and determine optimal grasping positions, even under challenging conditions. The results highlight the model’s ability to outperform existing methods, making it highly suitable for applications such as human-robot collaboration and precise object handling. This advancement has significant implications for industries like manufacturing, logistics, and healthcare, where robots must interact with objects quickly and accurately. Practitioners can adopt this method to enhance robotic performance, reduce errors, and improve operational efficiency. Future work could focus on testing the model in more complex environments and adapting it for real-time deployment in dynamic settings.

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Federated Learning-Enhanced Edge Deep Learning Model for EMG-Based Gesture Recognition in Real-Time Human-Robot Interaction. IEEE Sensors Journal. ISSN 1530-437X. 25(5), p. 9139–9151. doi: 10.1109/JSEN.2025.3529841. Full text in Research Archive Show summary

  Electromyography (EMG)-based gesture detection plays a crucial role in human-robot interaction (HRI), providing a seamless interface for controlling robotic systems through muscle activity. Despite its potential, EMG systems face significant challenges related to the security and privacy of sensitive biometric data, as well as the computational limitations of deploying deep learning (DL) models on edge devices. To address these issues, we propose a federated learning (FL)-based DL model for EMG gesture recognition, specifically designed for edge devices. Our model utilizes a custom dataset collected using a Mindrove eight-channel EMG armband, capturing eight distinct hand gestures—rest, move left, move right, move down, move up, open fingers, close fist, and twist hand—from ten subjects with seven repetitions each, ensuring diverse and robust data for training. During preprocessing, a bandpass filter (50–450 Hz) was applied to remove noise and enhance signal quality, followed by a short-term frequency transform (STFT) with a 200-ms sample time and 50% overlap to extract relevant features from the EMG signals. The dataset was segmented into training and testing sets with a 70/30 split for evaluation. We evaluate several FL techniques, including FedAvg, FedProx, and FedSGD, demonstrating that FedAvg achieves the highest accuracy of 96.92% without quantization with Scenario 9 (15 epochs, 20 rounds) with minimal communication overhead. Additionally, our model is quantized, resulting in an 89% reduction in size and a high accuracy of 95.99%, representing a minimal loss of 0.93%, making it ideal for edge deployment without compromising performance. A comparative analysis with other DL models, such as multiconvolutional residual networks (MCRNs), multiconvolutional neural networks (MCNNs), temporal convolutional networks (TCNs), and InceptionNet, shows that our approach outperforms these models in both accuracy and efficiency. Experimental results validate the high accuracy of our model in both training/testing and real-time disaster scenario simulations using the Spot robot. The proposed solution provides a secure, efficient, and highly accurate framework for EMG-based gesture recognition on edge devices, ideal for HRI and assistive technologies such as in search and rescue operations.

• Zafar, Muhammad Hamza; Poursina, Mohammad; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Trajectory Optimisation of a Robotic Manipulator Using a Novel Evolutionary Intelligence Algorithm. In Scarpa, Marco; Cavalieri, Salvatore; Serrano, Salvatore & Vita, Fabrizio De (Ed.), Proceedings of the 39th ECMS International Conference on Modelling and Simulation (ECMS 2025), June 24th - June 27th, 2025, Catania, Italy. ECMS European Council for Modelling and Simulation. ISSN 9783937436869. p. 93–99. doi: 10.7148/2025-0093. Full text in Research Archive Show summary

  In this paper, we present a novel approach for Proportional–Integral–Derivative (PID) tuning of a robotic manipulator, modeled using the Lagrange method and validated through comprehensive modeling and simulation. To optimize the PID gains, we developed a hybrid algorithm that combines Greylag Goose Optimization (GGO) with the Sine Cosine Algorithm (SCA), leveraging the strengths of both optimization techniques. The proposed hybrid algorithm, termed GGOSCA, was tested against GGO and Particle Swarm Optimization (PSO) using three objective functions: Lyapunov Based Function (LBF), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE). The results demonstrated that GGOSCA outperforms both GGO and PSO across all objective functions. Specifically, GGOSCA achieved the lowest costs of 0.1018, 0.2484, and 0.6601 for LBF, IAE, and ITAE, respectively, compared to 0.1022, 0.2580, and 0.6939 for GGO, and 0.1023, 0.2660, and 0.7273 for PSO. The superior performance of GGOSCA highlights its effectiveness in balancing exploration and exploitation, making it well-suited for complex control tasks such as PID tuning in robotic systems. This novel combination of GGO and SCA provides a robust and efficient solution for optimizing control parameters in dynamic environments, demonstrating its potential through rigorous modeling and simulation.

• Økter, Martin Bjaadal & Sanfilippo, Filippo (2025). A ROS-Based Framework for Low-Cost Real-Time Haptic Feedback in Human-Robot Teaming. In Scarpa, Marco; Cavalieri, Salvatore; Serrano, Salvatore & Vita, Fabrizio De (Ed.), Proceedings of the 39th ECMS International Conference on Modelling and Simulation (ECMS 2025), June 24th - June 27th, 2025, Catania, Italy. ECMS European Council for Modelling and Simulation. ISSN 9783937436869. p. 677–684. doi: 10.7148/2025-0677. Full text in Research Archive Show summary

  The evolution from human-robot interaction (HRI) to human-robot collaboration (HRC), and ultimately to human-robot teaming (HRT), represents a significant paradigm shift in how humans and robots work together. HRI initially framed robots as tools under human command, while HRC introduced greater autonomy in shared tasks. Today, HRT reimagines robots as equal team members, capable of adaptive communication and synchronized decision-making. A critical challenge in achieving effective HRT is developing haptic feedback systems that provide operators with real-time sensory information. This work presents a low-cost software-driven haptic feedback framework using ROS and micro-ROS to enhance realtime control in HRT environments. Unlike current costly, hardware-specific solutions, the proposed framework emphasizes accessibility and flexibility. System validation through precise force control tasks demonstrates the framework’s potential to advance HRT through cost-effective, software-centric solutions.

• Sanfilippo, Filippo; Zafar, Muhammad Hamza; Wiley, Timothy & Zambetta, Fabio (2025). From caged robots to high-fives in robotics: Exploring the paradigm shift from human–robot interaction to human–robot teaming in human–machine interfaces. Journal of manufacturing systems. ISSN 0278-6125. 78, p. 1–25. doi: 10.1016/j.jmsy.2024.10.015. Full text in Research Archive Show summary

  Multi-modal human–machine interfaces have recently undergone a remarkable transformation, progressing from simple human–robot interaction (HRI) to more advanced human–robot collaboration (HRC) and, ultimately, evolving into the concept of human–robot teaming (HRT). The aim of this work is to delineate a progressive path in this evolving transition. A structured, position-oriented review is proposed. Rather than aiming for an exhaustive survey, our objective is to propose a structured approach in a field that has seen diverse and sometimes divergent definitions of HRI/C/T in the literature. This conceptual review seeks to establish a unified and systematic framework for understanding these paradigms, offering clarity and coherence amidst their evolving complexities. We focus on integrating multiple sensory modalities — such as visual, aural, and tactile inputs — within human–machine interfaces. Central to our approach is a running use case of a warehouse workflow, which illustrates key aspects including modelling, control, communication, and technological integration. Additionally, we investigate recent advancements in machine learning and sensing technologies, emphasising robot perception, human intention recognition, and collaborative task engagement. Current challenges and future directions, including ethical considerations, user acceptance, and the need for explainable systems, are also addressed. By providing a structured pathway from HRI to HRT, this work aims to foster a deeper understanding and facilitate further advancements in human–machine interaction paradigms.

• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Emotion recognition with a Randomized CNN-multihead-attention hybrid model optimized by evolutionary intelligence algorithm. Array. 26. doi: 10.1016/j.array.2025.100401. Full text in Research Archive Show summary

  Emotion recognition systems are vital for various applications, yet existing models often face limitations in computational efficiency and accuracy, especially when handling complex emotional expressions in sequential data. To address these challenges, we propose an innovative emotion recognition framework that integrates a Randomised Convolutional Neural Network (RCNN) with a Multi-Head Attention model, further optimized by the Football Team Training Algorithm (FTTA) metaheuristic to enhance network parameters effectively. The RCNN, characterized by fixed random weights in its convolutional layers, efficiently extracts features from facial landmarks, enabling robust and diverse feature extraction while reducing computational load. This structure is complemented by a multi-head attention mechanism that processes temporal dynamics in emotion data, with both components optimized through FTTA to balance exploration and exploitation. Our hybrid model undergoes rigorous testing on a widely recognized emotion recognition dataset, outperforming conventional fully trainable models and alternative architectures. The results indicate a substantial improvement in classification accuracy, with an overall accuracy of 99%, and a significant reduction in computational demands, achieving a 65% faster training time on average compared to state-of-the-art models. These enhancements confirm the model’s efficiency and robustness across various emotional classifications. The synergy between the RCNN’s fixed-weight feature extraction and FTTA’s optimization capabilities demonstrates a powerful solution for emotion recognition systems. The combination of accuracy and efficiency renders our model suitable for real-world applications, particularly in fields like healthcare and mental health monitoring, where real-time emotion detection can have significant impacts.

• Bukhari, Syed Muhammad Salman; Dahmani, Nadia; Gyawali, Sujan; Zafar, Muhammad Hamza; Sanfilippo, Filippo & Raja, Kiran (2025). Optimizing fire detection in remote sensing imagery for edge devices: A quantization-enhanced hybrid deep learning model. Displays (Guildford). ISSN 0141-9382. 89. doi: 10.1016/j.displa.2025.103070. Full text in Research Archive Show summary

  Wildfires are increasing in frequency and severity, presenting critical challenges for timely detection and response, particularly in remote or resource-limited environments. This study introduces the Inception-ResNet Transformer with Quantization (IRTQ), a novel hybrid deep learning (DL) framework that integrates multiscale feature extraction with global attention and advanced quantization. The proposed model is specifically optimized for edge deployment on platforms such as unmanned aerial vehicles (UAVs), offering a unique combination of high accuracy, low latency, and compact memory footprint. The IRTQ model achieves 98.9% accuracy across diverse datasets and shows strong generalization through cross-dataset validation. Quantization significantly reduces the parameter count to 0.09M and memory usage to 0.13 MB, enabling real-time inference in 3 ms. Interpretability is further enhanced through Grad-CAM visualizations, supporting transparent decisionmaking. While achieving state-of-the-art performance, the model encounters challenges in visually ambiguous fire-like regions. To address these, future work will explore multi-modal inputs and extend the model towards multi-class classification. IRTQ represents a technically grounded, interpretable, and deployable solution for AI-driven wildfire detection and disaster response.

• Gonsholt, Alf Stian Sundo; Greca, Eivind Enea; Haddad, Mustapha; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2025). Enhanced Human-Robot Teaming Through Attention Multi Convolutional Neural Network-Based Multi-Modal Sensor Fusion for Hand Gesture Recognition and Orientation Control. In Bui, Tung (Eds.), Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS), Hilton Waikoloa Village, January 7-10, 2025. University of Hawai'i at Manoa. ISSN 9780998133188. p. 590–599. Full text in Research Archive Show summary

  Our study aims at enhancing Human-Robot Interaction, Collaboration, and Teaming (HRI/C/T) in industrial automation by developing a novel framework for real-time gesture control of a robotic hand. We use an Inertial Measurement Unit (IMU) sensor for precise orientation control of the end effector, and surface Electromyography (sEMG) sensors to detect muscle movements. The sEMG signals are processed by an Attention-based Multi Convolutional Neural Network (A-MCNN) for accurate gesture detection, enabling the robotic hand to mimic these gestures in real-time. Our method achieves notable results for gesture recognition, with the A-MCNN model attaining an accuracy of 97.89%, a precision of 97.49%, a recall of 97.71%, and an F1 score of 97.65%. This integration of IMU and sEMG technologies with advanced neural networks creates a responsive and intuitive control mechanism, improving safety, usability, and interaction of collaborative robots in shared workspaces. Our approach aims to transition towards Human-Robot Teaming (HRT), significantly advancing the seamless and safe integration of robots in industrial environments, enhancing productivity and collaboration.

• Sanfilippo, Filippo; Tataru, Marius; Hua, Minh Tuan; Johansson, Inge Johan Straumsøy & Andone, Diana (2025). Gamifying Cultural Immersion: Virtual Reality (VR) and Mixed Reality (MR) in City Heritage. IEEE Transactions on Games (TG). ISSN 2475-1502. 17(4), p. 893–911. doi: 10.1109/TG.2025.3553712. Full text in Research Archive Show summary

  The exploration of city culture and heritage has gone through a fundamental transition in today's digital world, boosted by the introduction of extended reality (XR) technologies, such as virtual reality (VR), mixed reality (MR), and augmented reality. These developments have provided new opportunities for individuals to profoundly engage with historical narratives and artistic expressions inherent in urban environments. Despite these technical advancements, a critical research gap remains in properly combining these immersive technologies with gamification principles to improve cultural exploration. This study seeks to fill this gap by studying the integration of gamification into XR settings, with the goal of increasing participant engagement, cooperation, and interaction while digging into the various layers of a city's history and artistic heritage. Two complementary use cases are presented: one centred on VR and the other on MR, both of which provide unique immersive experiences customized to exploring city culture. Gamification ideas are implemented into these use cases, with game elements used to encourage user involvement and participation within historical and artistic settings. Students are actively involved in the development of cultural heritage applications, highlighting the value of educational engagement. To assess the success and validity of this approach, a system usability scale (SUS) questionnaire is distributed to users participating in these immersive experiences. The survey findings evaluate user perceptions, satisfaction levels, and the effectiveness of gamification aspects in improving their understanding and connection to the city's heritage. The VR application received a score of 71.77 (out of 100), while the MR application received a score of 65.94 (out of 100), both being very close to the average SUS score of 68. Moreover, to improve the rigour of our evaluation, the user engagement scale short form (UES-SF) is also incorporated. The UES results indicate that participants felt more immersed in the MR application (4.33) compared to the VR application (3.57). This difference may be attributed to the MR application's ability to integrate interactive elements with the real-world environment, enhancing the sense of presence and relevance for users. Both applications had similar perceived usability scores, while the MR app slightly outperformed the VR app in aesthetics and rewarding factors, suggesting a better overall user experience.

• Sanfilippo, Filippo; Fossum, Mariann & Hansen, Michael Rygaard (2025). Redefining Care and Work Practices: The Role of Autonomy in the Future of Nursing Homes. In Paolis, Lucio Tommaso De; Arpaia, Pasquale & Sacco, Marco (Ed.), Extended Reality. Springer. ISSN 9783031977718. p. 355–370. doi: 10.1007/978-3-031-97781-7_26. Full text in Research Archive Show summary

  Nursing homes face increasing challenges due to workforce shortages, ageing populations, and rising care demands. This paper explores the dynamics of human-robot interaction (HRI) in nursing home environments, focusing on how autonomous robots engage with nurses and residents in nursing homes. We examine the role of trust-building and emotional engagement in caregiving scenarios, where patient well-being depends on effective HRI. The potential for autonomous systems to support decision-making while maintaining human oversight is investigated. Using the Autonomy Levels for Unmanned Systems (ALFUS) framework, we assess care robots’ autonomy in terms of mission complexity (MC), environmental complexity (EC), and external system independence (ESI). Additionally, the Autonomy and Technology Readiness Assessment (ATRA) method supports gradual capability enhancement, providing a roadmap to higher autonomy while ensuring safety and reliability. Based on this established methodology, we introduce the Nursing Home Interaction Framework (NHIF), a novel conceptual framework defining the roles of actors involved, to connect theoretical insights with real-world applications. This work highlights the significance of human-robot trust in elderly care scenarios and proposes directions for future research on collaborative decision-making in nursing home HRI.

• Sanfilippo, Filippo; Wiley, Timothy & Rousi, Rebekah (2025). RoboCup Soccer Autonomy Uprising: How Crowds, Referees, and Humanoid Robots Are Redefining the Future of Human-Robot Interaction. In NN, NN (Eds.), HRI '25: Proceedings of the 2025 ACM/IEEE International Conference on Human-Robot Interaction. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350378931. p. 1131–1139. doi: https:/doi.org/https:/dl.acm.org/doi/10.5555/3721488.3721634. Full text in Research Archive Show summary

  This paper explores the dynamics of Human-Robot Interaction (HRI) in public spaces, focusing on how humanoid robots engage with human crowds in the competitive RoboCup Soccer environment. We examine the role of spectatorship, where emotional engagement arises through indirect observation of engineering-driven competition, drawing parallels between human soccer and robot sports. The potential for autonomous systems to elicit collective emotions and systematically study such experiences is investigated. Using the Autonomy Levels for Unmanned Systems (ALFUS) framework, we assess RoboCup soccer robots' autonomy in terms of mission complexity (MC), environmental complexity (EC), and external system independence (ESI). Additionally, the Autonomy and Technology Readiness Assessment (ATRA) method supports gradual capability enhancement, providing a roadmap to higher autonomy. Based on this established methodology, we introduce the Robot-Crowd Interaction Framework (R-CIF), a novel conceptual framework defining the roles of actors involved, to connect theoretical insights with real-world applications. This work highlights the significance of crowd affectivity in robotic sports to boost public engagement and proposes directions for future research on collective emotional dynamics in HRI.

• Sanfilippo, Filippo; Moosavi, Syed Kumayl Raza & Zafar, Muhammad Hamza (2025). Autonomy Uprising: How Civilians, First Responders, and Robots Are Redefining the Future of Search-and-Rescue (SAR). In NN, NN (Eds.), 2025 9th International Conference on Robotics and Automation Sciences: AI‑Powered Robotics Advancements (ICRAS 2025). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350392487. p. 1–8. doi: 10.1109/ICRAS65818.2025.11108899. Full text in Research Archive Show summary

  This paper explores the dynamics of human-robot interaction (HRI) in emergency response scenarios, focusing on how autonomous robots engage with civilians and first responders in search-and-rescue (SAR) operations. We examine the role of trust-building and emotional engagement in crisis situations, where human lives depend on effective HRI. The potential for autonomous systems to support decision-making while maintaining human oversight is investigated. Using the Autonomy Levels for Unmanned Systems (ALFUS) framework, we assess SAR robots' autonomy in terms of mission complexity (MC), environmental complexity (EC), and external system independence (ESI). Additionally, the Autonomy and Technology Readiness Assessment (ATRA) method supports gradual capability enhancement, providing a roadmap to higher autonomy while ensuring safety and reliability. Based on this established methodology, we introduce the Emergency Response Interaction Framework (ERIF), a novel conceptual framework defining the roles of actors involved, to connect theoretical insights with real-world applications. This work highlights the significance of human-robot trust in crisis scenarios and proposes directions for future research on collective decision-making in emergency response HRI.

• Aftab, Muhammad Faisal; Bounoua, Wahiba; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2025). Improved Stiction Detection via Hybrid Residual Embedded Inception Module Networks. IFAC-PapersOnLine. ISSN 2405-8971. 59(6), p. 145–150. doi: 10.1016/j.ifacol.2025.07.136. Full text in Research Archive
• Sanfilippo, Filippo; Salvietti, Gionata; Blažauskas, Tomas; Gabriele, Guido; Zafar, Maria & Hua, Minh Tuan [Show all 14 contributors for this article] (2025). Integrating VR, AR, and Haptics in Basic Surgical Skills Training: A Review and Perspective. IEEE Access. 13, p. 99203–99220. doi: 10.1109/ACCESS.2025.3574265. Full text in Research Archive Show summary

  Traditional surgical training faces challenges related to sustainability, cost, and ethical considerations. Limited access to resources, ethical concerns about cadaver-based learning, limited availability of surgical cases, and the high costs associated with hands-on training impede the effectiveness and accessibility of surgical education. Despite these challenges, the need for direct tactile experience remains critical, as it fosters the development of essential motor skills and tactile abilities vital for surgical practice. However, advancements in immersive technologies such as virtual reality (VR), augmented reality (AR), and haptics offer a promising solution to address these challenges. VR and AR technologies offer realistic, controlled environments where learners can visualise anatomical structures and practice surgical procedures without risks. The integration of haptic feedback in these immersive simulations further enriches the learning process by adding the sense of touch and physical interaction, aiding in mastering critical surgical skills. The incorporation of immersive technologies in surgical training presents an opportunity to address the constraints of conventional methods. These technologies offer the possibility of creating platforms that replicate real-life surgical scenarios, allowing learners to hone their skills and build confidence in a safe and controlled environment. Moreover, immersive learning offers an ethical alternative to cadaver-based training and extends access to training materials irrespective of geographical limitations. Such an approach allows for step-based training, where students may begin acquiring skills through immersive learning, then move to special surgical simulators, and finally to patients. This article deals with the impact of immersive technologies such as VR, AR, and haptics on reshaping conventional surgical training, by focusing specifically on basic surgical skills. Targeting educators, medical experts, and involved parties, the article aims to stimulate additional exploration and adoption of immersive technologies to elevate surgical competencies and patient care standards.

• Remenyi, Armin; Kuts, Vladimir; Pizzagalli, Simone Luca; Tepljakov, Aleksei; Han, Dai-In Danny & Johnston, Ryan [Show all 7 contributors for this article] (2025). From Virtual to Reality: A Structured Framework for Training Humanoid Robots for Elderly Care Using Learning from Demonstration. In Paolis, Lucio Tommaso De; Arpaia, Pasquale & Sacco, Marco (Ed.), Extended Reality. Springer. ISSN 9783031977718. p. 193–209. doi: 10.1007/978-3-031-97772-5_13. Full text in Research Archive Show summary

  The increasing demand for elderly care presents substantial challenges for global healthcare systems, necessitating scalable and adaptive technological solutions. Humanoid robots hold significant potential in assisting elderly users with mobility support, medication management, and social engagement. However, their effectiveness is often constrained by current robotic learning methods, which predominantly rely on scripted or teleoperated behaviors, limiting their adaptability in dynamic caregiving contexts. This conceptual paper introduces a structured Learning from Demonstration (LfD) framework. It leverages Virtual Reality (VR) to capture realistic, embodied demonstrations tailored explicitly for elderly care tasks. We critically discuss key challenges inherent to translating VR-trained behaviors to real-world robot deployment—such as embodiment discrepancies, sensor limitations, and environmental variability—and propose iterative methods to mitigate these issues. This framework explicitly aims to stimulate scholarly discussion, paving the way for subsequent empirical validation and prototyping. Our work thus establishes foundational guidelines and specific research questions for future studies on VR-driven robotic adaptation in elderly care scenarios.

• Das, Opy; Dahlioui, Dounia; Zafar, Muhammad Hamza; Akhtar, Naureen; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Ultra-short term PV power forecasting under diverse environmental conditions: A case study of Norway. Energy Conversion and Management: X. 27. doi: 10.1016/j.ecmx.2025.101072. Full text in Research Archive Show summary

  Accurate short-term solar forecasting is critical for power plant operations, grid balancing, real-time dispatching, automatic generation control, and energy trading. In Norway, where solar radiation is limited in winter and highly variable in summer, accurate predictions are essential. This study focuses on ultra-short-term forecasting of solar radiation and power output from a 37.8 kWp solar photovoltaic (PV) power plant at the University of Agder (UiA), Grimstad (58.335322°N, 8.577718°E), in southern Norway. We propose a novel forecasting model that integrates Spatial Attention, Temporal Attention, Self-Attention, CNN, and BiLSTM architectures to enhance prediction accuracy. Using a custom dataset collected from the UiA PV plant, the model’s effectiveness was validated through comprehensive ablation studies and comparative analysis with state-of-the-art methods. The proposed model achieved a low RMSE of 0.162 kW using seven days of data, demonstrating its superiority in predicting short-term PV power outputs and associated uncertainties, outperforming conventional forecasting techniques.

• Sanfilippo, Filippo; Lionel, Robert; Sangseok, You & Connor, Esterwood (2025). Introduction to the Minitrack on Human-Robot Interaction and Collaboration. In Bui, Tung (Eds.), Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS), Hilton Waikoloa Village, January 7-10, 2025. University of Hawai'i at Manoa. ISSN 9780998133188. p. 576–579. Full text in Research Archive Show summary

  This minitrack explores the forefront of Human-Robot Interaction (HRI), with a focus on the ongoing evolution toward seamless Human-Robot Collaboration (HRC). By addressing the latest breakthroughs in methodologies, technologies, and research, this minitrack aims to shed light on the dynamic and rapidly evolving relationship between humans and robots. Experts will present state-of-the-art advancements that are driving this transformation, fostering deeper integration and cooperation between humans and robotic systems.

• Juric, Radmila; Ronchieri, Elisabetta; Sanfilippo, Filippo & Bihl, Trevor (2025). Introduction to the Minitrack on Intelligent Edge Computing. In Bui, Tung (Eds.), Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS), Hilton Waikoloa Village, January 7-10, 2025. University of Hawai'i at Manoa. ISSN 9780998133188. p. 7247–7247. Full text in Research Archive
• Langås, Even Falkenberg; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2025). Exploring the synergy of human-robot teaming, digital twins, and machine learning in Industry 5.0: a step towards sustainable manufacturing. Journal of Intelligent Manufacturing. ISSN 0956-5515. 37, p. 99–1022. doi: 10.1007/s10845-025-02580-x. Full text in Research Archive Show summary

  Sustainable manufacturing remains a central objective of Industry 5.0. By successfully implementing harmonic human-robot teams in intelligent industrial systems, the efficiency and well-being of human workers can be increased. Achieving this requires a gradual approach from caged robots to advanced, seamless collaboration between humans and robots. Initially, that means transitioning to human-robot interaction (HRI) where there is an exchange of commands between the human and the robot. Further advancements within safety considerations, including collision avoidance through advanced machine vision, enable the exchange of workspace that defines human-robot collaboration (HRC). The next stage is physical HRC (pHRC) which requires safe and controlled exchange of forces through impedance and admittance control. Finally, this paper describes human-robot teaming (HRT), which is defined by the exchange of solutions as teammates. This is enabled by combining cutting-edge technologies such as digital twin (DT), advanced vision sensors, machine learning (ML) algorithms and mixed reality (MR) human–machine interfaces for operators. A key contribution of this work is reviewing the integration of HRT with DT and ML, highlighting how these technologies enable seamless perception, prediction, and decision-making in human-centric industrial systems. By reviewing these technologies, the paper highlights current challenges, limitations and research gaps within the field of HRT and suggests potential future possibilities for HRT, such as advanced disassembly of used goods for a more sustainable manufacturing industry.

• Zafar, Muhammad Hamza; Bukhari, Syed Muhammad Salman; Houran, Mohamad Abou; Mansoor, Majad; Khan, Noman Mujeeb & Sanfilippo, Filippo (2024). DeepTimeNet: A novel architecture for precise surface temperature estimation of lithium-ion batteries across diverse ambient conditions. Case Studies in Thermal Engineering. 61. doi: 10.1016/j.csite.2024.105002. Full text in Research Archive
• Sanfilippo, Filippo & Zafar, Muhammad Hamza (2024). Human-Robot Teaming: A Universal Controller with Multi-Modal Feedback for Emergency Response Robots. In NN, . (Eds.), 9th International Conference on Robotics and Automation Engineering (ICRAE 2024). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331518301. p. 181–187. doi: 10.1109/ICRAE64368.2024.10851531. Full text in Research Archive Show summary

  This work presents the development of a universal wireless controller that supports the Robot Operating System (ROS), with a focus on enhancing system reliability and safety in Human-Robot Teaming (HRT). The controller is designed to provide multi-modal auditory, visual, and tactile feedback mechanisms, ensuring robust and reliable communication between human operators and various robots during collaborative emergency scenarios. The primary objective is to create an intuitive and unified interface that not only facilitates efficient collaboration but also significantly enhances the safety of operations by minimising the risk of human errors in dynamic and unpredictable environments. By integrating ROS capabilities with diverse sensorial feedback modalities, the controller improves situational awareness (SA) and operational reliability, enabling human operators, such as first responders, to maintain control and makeinformed decisions under pressure. A use case involving the navigation of a quadruped robot through an obstacle-filled environment is presented to demonstrate the controller’s practical benefits in ensuring safety and reliability during complex tasks. The sequence of operations, including safe navigation, reliable victim proximity indication, and secure emergency stops, highlights the controller’s effectiveness in real-world scenarios, emphasising its role in promoting both system reliability and operational safety.

• Zafar, Muhammad Hamza; Langås, Even Falkenberg; Aftab, Muhammad Faisal & Sanfilippo, Filippo (2024). Enhanced Intrusion Detection in Robot Operating Systems via Grid Search Based Multi-Head Attention Stacked Convolutional Network. IEEE International Conference on Automation Science and Engineering. ISSN 2161-8070. p. 3880–3885. doi: 10.1109/CASE59546.2024.10711785. Full text in Research Archive
• Hua, Minh Tuan; Sveen, Emil Mühlbradt; Schlanbusch, Siri Marte & Sanfilippo, Filippo (2024). Robust-Adaptive Two-Loop Control for Robots with Mixed Rigid-Elastic Joints. In NN, . (Eds.), International Conference on Intelligent Robots and Systems (IROS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350377705. p. 11332–11339. doi: 10.1109/IROS58592.2024.10802178. Full text in Research Archive Show summary

  In robotics, while rigid joints are common due to their accuracy and fast response ability, elastic joints are wellknown for their safety when interacting with the environment. To harmonise the advantages of these joint types, robots with mixed rigid-elastic joints can be considered. In this paper, a robust-adaptive two-loop control algorithm is proposed to control this type of hybrid robots when there are uncertainties in system parameters. In the outer loop, a robust control algorithm is proposed to deal with the uncertainties in the parameters of the joint dynamics, together with an adaptive controller for the rigid joints. In the inner loop, another robust control algorithm is proposed to handle the uncertainties in system parameters of the elastic joint’s motor contribution, and a similar adaptive control algorithm is presented to manipulate the elastic joints’ motors. The stability of the system is assured by Lyapunov’s stability theory. Finally, simulations are conducted to verify the proposed control algorithm.

• Păcurar, Răzvan Ioan; Sanfilippo, Filippo; Økter, Martin Bjaadal; Băilă, Diana-Irinel; Zaharia, Cătălin & Nicoară, Adrian Ionuţ [Show all 14 contributors for this article] (2024). Use of high-performance polymeric materials in customized low-cost robotic grippers for biomechatronic applications: experimental and analytical research. Frontiers in Materials. 11. doi: 10.3389/fmats.2024.1304339. Full text in Research Archive
• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2024). Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection. Frontiers in Robotics and AI. 11, p. 01–16. doi: 10.3389/frobt.2024.1356345. Full text in Research Archive
• Baila, Diana Irinel; Sanfilippo, Filippo; Savu, Tom; Górski, Filip; Radu, Ionut Cristian & Zaharia, Catalin [Show all 9 contributors for this article] (2024). 3D printing of personalised stents using new advanced photopolymerizable resins and Ti-6Al-4V alloy. Rapid prototyping journal. ISSN 1355-2546. 30(4), p. 696–710. doi: 10.1108/RPJ-10-2023-0360. Full text in Research Archive
• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2024). Collaborative robots (cobots) for disaster risk resilience: a framework for swarm of snake robots in delivering first aid in emergency situations. Frontiers in Robotics and AI. 11, p. 1–12. doi: 10.3389/frobt.2024.1362294. Full text in Research Archive
• Zafar, Muhammad Hamza; Khan, Noman Mujeeb; Houran, Mohamad Abou; Mansoor, Majad; Akhtar, Naureen & Sanfilippo, Filippo (2024). A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature. Energy. ISSN 0360-5442. 292, p. 1–22. doi: 10.1016/j.energy.2024.130584. Full text in Research Archive
• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Mansoor, Majad & Sanfilippo, Filippo (2024). An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease. Healthcare Analytics. 7. doi: 10.1016/j.health.2024.100374. Full text in Research Archive Show summary

  Cardiovascular disease is the leading cause of death worldwide, including critical conditions such as blood vessel blockage, heart failure, and stroke. Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. The SCNN provides robust feature extraction, while LFGOA enhances the model by optimizing hyperparameters, improving classification accuracy, and reducing overfitting. The proposed approach is evaluated using four publicly available heart disease datasets, each representing diverse clinical and demographic features. Compared to traditional classifiers, including Regression Trees, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and standard Neural Networks, the SCNN-LFGOA consistently outperforms these methods. The results highlight that the SCNN-LFGOA achieves an average accuracy of 99%, with significant improvements in specificity, sensitivity, and F1-Score, showcasing its adaptability and robustness across datasets. This study highlights the SCNN-LFGOA’s potential as a transformative tool for early and accurate heart disease prediction, contributing to improved patient outcomes and more efficient healthcare resource utilization. By combining deep learning with an advanced optimization technique, this research introduces a scalable and effective solution to a critical healthcare problem.

• Chitikena, Hareesh; Gravdahl, Irja; Pettersen, Kristin Ytterstad; Mohammadi, Alireza; Sanfilippo, Filippo & Stavdahl, Øyvind [Show all 7 contributors for this article] (2024). Adaptive manoeuvring control for planar snake robots in uncertain friction environments. In NN, . (Eds.), American Control Conference. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350382655. p. 2844–2850. doi: 10.23919/ACC60939.2024.10644564. Full text in Research Archive Show summary

  The locomotion dynamics of a snake robot are greatly influenced by the way it interacts with its surroundings, particularly in terms of friction. Given the variable frictional characteristics of the environments in which holonomic snake robots locomote, this paper introduces a novel friction-adaptive body-shape controller that dynamically adjusts the robot's locomotion parameters in response to changes in friction. One of this paper's contributions is based on deriving a novel linear regression-based representation of the ground friction forces acting on the snake robot, which results in coupled dynamics between the gait tracking and friction parameter estimation errors. Using these coupled dynamics and our proposed friction adaptation law, we prove practical stability of the gait tracking and friction parameter estimation errors in the general case, as well as asymptotic convergence of the gait tracking errors and boundedness of the friction parameter estimation errors in a special case. Simulation results show that the proposed controller is stable and that the gait tracking error and friction parameter estimation errors are converging and bounded.

• Langås, Even Falkenberg; Zafar, Muhammad Hamza; Nyberg, Svein Olav Glesaaen & Sanfilippo, Filippo (2024). Human Trajectory Simulation in Industrial Settings Using the Ornstein-Uhlenbeck Process and Deep Learning Based Classification. In NN, . (Eds.), International Conference on Automation, Robotics and Applications (ICARA). Institute of Electrical and Electronics Engineers (IEEE). ISSN 9798350394252. p. 427–432. doi: 10.1109/ICARA60736.2024.10553211. Full text in Research Archive Show summary

  This paper presents a novel method of simulating human trajectories using the Ornstein-Uhlenbeck (OU) process in addition to deep learning (DL) based classification. The OU process is a stochastic process and is used in this paper to simulate the movement of a person on a typical factory floor. This work aims at developing systems that increase machines' awareness of people and make predictions about their behaviour to improve efficiency and safety in industrial settings. Sequences of simulated 2D coordinates of people moving on the factory floor are generated. Successively, these synthetic data are used to classify the path that the human is following, using a stacked long short-term memory (LSTM) network and a stacked bidirectional LSTM (BiLSTM) network. The results from this study suggest that, for such applications, it should be possible to predict future movements in 2D for human-robot collaboration (HRC) and teaming (HRT).

• Zafar, Muhammad Hamza; Langås, Even Falkenberg & Sanfilippo, Filippo (2024). Real-Time Gesture-Based Control of a Quadruped Robot Using a Stacked Convolutional Bi-Long Short-Term Memory (Bi-LSTM) Neural Network. In NN, . (Eds.), International Conference on Automation, Robotics and Applications (ICARA). Institute of Electrical and Electronics Engineers (IEEE). ISSN 9798350394252. p. 81–86. doi: 10.1109/ICARA60736.2024.10553163. Full text in Research Archive
• Zafar, Muhammad Hamza; Langås, Even Falkenberg; Nyberg, Svein Olav Glesaaen & Sanfilippo, Filippo (2024). Multimodal Fusion of EEG and EMG Signals Using Self-Attention Multi-Temporal Convolutional Neural Networks for Enhanced Hand Gesture Recognition in Rehabilitation, International Conference on Omni-layer Intelligent Systems (COINS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350349597. p. 245–249. doi: 10.1109/COINS61597.2024.10622144. Full text in Research Archive Show summary

  In this work, we introduce an innovative approach to hand gesture recognition aimed at rehabilitation applications, utilising the synergistic potential of multimodal data fusion from electroencephalogram (EEG) and electromyogram (EMG) sensors. Our approach exploits the strength of Self-Attention Multi-Temporal Convolutional Networks (SAMTCN), which adeptly combine the distinct and complementary insights provided by EEG and EMG signals. The core of our methodology is the strategic application of self-attention mechanisms with multi-temporal convolutional architectures. This design choice allows our model to capture and analyse temporal patterns in multimodal data with unprecedented precision, significantly enhancing its ability to generalise to new, unseen data. The effectiveness of our approach is evidenced by the model's exceptional performance, achieving an accuracy of over 97% in recognising diverse hand gestures. This high level of accuracy highlights the model's potential to revolutionise how interactions are facilitated in rehabilitation contexts.

• Langås, Even Falkenberg; Bukhari, Halima Zahra; Hagen, Daniel; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2024). Inclusive Digital Twins with Edge Computing, Cloud Communication and Virtual Reality to Achieve Remote Human-Robot Interaction. In NN, . (Eds.), 12th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331517519. p. 323–329. doi: 10.1109/ICCMA63715.2024.10843896. Full text in Research Archive Show summary

  Digital twins, advanced robotics, edge computing, and immersive technologies have come together to create novel solutions that increase flexibility and operating efficiency. This work is motivated by the need to harness these advancements to develop a robust architecture that supports edge intelligence and real-time remote monitoring and control. The aim of this work is to define a comprehensive framework for digital twins of complex mechatronic systems. The framework enables seamless connection between the physical environment and a virtual representation accessible from remote locations. Key components of the framework include the Robot Operating System 2 (ROS 2) for robot control, Node-RED for data processing and edge communication, a Message Queuing Telemetry Transport (MQTT) broker for cloud-based communication, and a virtual reality (VR) application for immersive interaction. A case study is presented to demonstrate the framework’s capabilities. Through the VR application, users can interact with a digital twin of a mechatronic system consisting of a collaborative robot, a programmable logic controller (PLC), a conveyor belt and numerous sensors. The user interface lets the operator manipulate the robot and monitor sensor data in real-time. Latency is measured to validate the performance of the framework, resulting in a mean latency of around 116 ms.

• Murtaza, Aitzaz Ahmed; Saher, Amina; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Aftab, Muhammad Faisal & Sanfilippo, Filippo (2024). Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study. Results in Engineering (RINENG). 24. doi: 10.1016/j.rineng.2024.102935. Full text in Research Archive
• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2024). Hierarchical Recurrent-Inception Residual Transformer (HRIRT) for Multidimensional Hand Force Estimation Using Force Myography Sensor. IEEE Sensors Letters. 8(9). doi: 10.1109/LSENS.2024.3431433. Full text in Research Archive
• Venčkauskas, Algimantas; Toldinas, Jevgenijus; Morkevičius, Nerijus & Sanfilippo, Filippo (2024). An Email Cyber Threat Intelligence Method Using Domain Ontology and Machine Learning. Electronics (Basel). 13(14), p. 1–22. doi: 10.3390/electronics13142716. Full text in Research Archive
• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Khan, Noman Mujeeb & Sanfilippo, Filippo (2024). FireNet: A Hybrid Deep Learning Approach for Enhanced Fire Detection in Remote Sensing Imagery. In NN, NN (Eds.), Intelligent Systems Conference (IntelliSys). Springer. ISSN 9783031663291. p. 1–15. doi: 10.1007/978-3-031-66329-1_1. Full text in Research Archive Show summary

  In this study, we address the critical task of early bushfire detection in remote areas through UAV imaging, a vital effort for protecting inhabitants, infrastructure, and ecosystems within the smart cities paradigm. We introduce FireNet, a deep learning framework that employs a hybrid Inception Residual Neural Network (Inception-ResNet) model, specifically designed for the rapid and precise classification of fire and non-fire regions in real-time image processing scenarios. FireNet synergises the strengths of both Inception and ResNet architectures, enhancing the model’s feature extraction capabilities while significantly reducing computational overhead. A distinctive aspect of FireNet is the adoption of the HardSwish activation function, demonstrating superior performance over the conventional Rectified Linear Unit (ReLU) in our fire detection cases. Through rigorous evaluation of a robust dataset, FireNet achieves an impressive accuracy of 96%, with a corresponding AUC of approximately 96%. These results not only affirm FireNet’s efficacy in accurately identifying fire and non-fire areas but also underscore its importance as a crucial tool for real-time fire monitoring and rescue operations, especially in the context of smart cities. With its outstanding efficiency and accuracy, FireNet represents a significant leap forward in the domain of fire detection technologies, highlighting the role of deep learning in enhancing urban resilience against fires.

• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Khan, Noman Mujeeb & Sanfilippo, Filippo (2024). Enhancing Alzheimer’s Disease Detection and Classification Through Federated Learning-Optimized Deep Convolutional Neural Networks on MRI Data. In NN, NN (Eds.), Intelligent Systems Conference (IntelliSys). Springer. ISSN 9783031663291. p. 693–712. doi: 10.1007/978-3-031-66428-1_44. Full text in Research Archive Show summary

  Early and accurate detection of Alzheimer’s Disease (AD) is vital for effective management and intervention. However, the subtle nuances of AD progression and the limited availability of comprehensive datasets pose significant diagnostic challenges. Our study introduces an innovative Deep Convolutional Neural Network (DCNN) model, optimised using Federated Learning (FL) for AD detection and staging from MRI images. This approach respects data privacy by enabling decentralised, multi-institutional model training, and demonstrates exceptional adaptability and precision. On the Open Access Series of Imaging Studies (OASIS) database, our FL-DCNN model achieved a remarkable 97% accuracy rate, suggesting significant potential for enhancing AD diagnostics and patient care.

• Hua, Minh Tuan; Park, Jong Hyeon & Sanfilippo, Filippo (2024). Modelling and Control of a Hybrid Robotic Arm with Mixed Rigid-Elastic Joints, 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10665130. Full text in Research Archive
• Moosavi, Syed Kumayl Raza; Sanfilippo, Filippo & Zafar, Muhammad Hamza (2024). Collaborative robots (cobots) for emergency situations: a snake robot as a team member for delivering first aid in emergency situations. In Bui, Tung X. (Eds.), Proceedings of the 57th Hawaii International Conference on System Sciences. The Hawaii International Conference on System Sciences (HICSS). ISSN 9780998133171. p. 443–452. Full text in Research Archive
• Sanfilippo, Filippo; Økter, Martin Bjaadal; Dale, Jørgen; Hua, Minh Tuan & Ottestad, Morten (2024). Revolutionising Prosthetics and Orthotics with Affordable Customisable Open-Source Elastic Actuators. In NN, . (Eds.), International Conference on Automation, Robotics and Applications (ICARA). Institute of Electrical and Electronics Engineers (IEEE). ISSN 9798350394252. p. 57–63. doi: 10.1109/ICARA60736.2024.10553120. Full text in Research Archive Show summary

  Collaborative robots, also known as cobots, have become progressively popular as they are designed to safely work alongside humans or in shared environments. The adoption of compliant actuators, which avoid damage in the event of an unintentional collision, is an important feature of their design. In prosthetic and orthotic applications, the use of compliant actuators, which minimise damage in the case of an inadvertent contact, is critical to ensuring safety and comfort for the user. In dynamic settings, elastic actuators are preferred over rigid ones, however most compliant cobots are currently too expensive or complex to construct for prosthetic and orthotic applications. To address this challenge, this study presents a low-cost sensorised elastic actuator design for prosthetics and orthotics. This is presented as an educational framework. The design is modular and can be quickly customised using 3D printing technology, allowing for efficient and cost-effective fabrication. Both the hardware and software are open-source, enabling easy access for students, researchers and practitioners. The design also allows for the introduction of impedance and admittance control techniques, providing even greater versatility to the system.

• Sanfilippo, Filippo; Økter, Martin Bjaadal; Dale, Jørgen; Hua, Minh Tuan; Zafar, Muhammad Hamza & Ottestad, Morten (2024). Open-source design of low-cost sensorised elastic actuators for collaborative prosthetics and orthotics. HardwareX. 19. doi: 10.1016/j.ohx.2024.e00564. Full text in Research Archive

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• Sanfilippo, Filippo; Fossum, Mariann & Hansen, Michael Rygaard (2026). Redefining Care and Work Practices: The Role of Autonomy in the Future of Nursing Homes | Springer Nature Link. Springer. ISBN 9783031977817. doi: 10.1007/978-3-031-97781-7_26. Full text in Research Archive Show summary

  Nursing homes face increasing challenges due to workforce shortages, ageing populations, and rising care demands. This paper explores the dynamics of human-robot interaction (HRI) in nursing home environments, focusing on how autonomous robots engage with nurses and...

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• Das, Opy; Sui, Xin; Sanfilippo, Filippo & Rudra, Souman (2025). Battery Time-Series Data Augmentation Using a GAF-Based Generative Framework. Full text in Research Archive
• Sanfilippo, Filippo (2025). Human-Robot Teaming, a Forward Leap into Real Life Applications. Full text in Research Archive
• Juric, Radmila; Ronchieri, Elisabetta; Sanfilippo, Filippo & Bihl, Trevor (2025). Introduction to the Intelligent Edge Computing Minitrack. Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS). ISSN 1530-1605. p. 7247–7247. Full text in Research Archive
• Sanfilippo, Filippo (2025). Kan denne roboten ta én prosent av legens arbeid? [Journal]. Tekna Magasinet. Full text in Research Archive
• Juric, Radmila; Ronchieri, Elisabetta & Sanfilippo, Filippo (2024). Introduction to Intelligent Edge Computing in Pervasive Environments Minitrack. Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS). ISSN 1530-1605. p. 7550–7550. Full text in Research Archive
• Sanfilippo, Filippo (2024). IEEE Region 8 Robot Championship,served as a referee for this competition. Full text in Research Archive
• Sanfilippo, Filippo & Zafar, Muhammad Hamza (2024). Hands-on Collaboration: Building Robot Muscle Memory. IEEE Transmitter. Full text in Research Archive
• Sanfilippo, Filippo (2024). Representing IEEE Norway Section. Full text in Research Archive
• Sanfilippo, Filippo (2024). Representing IEEE Norway Section. Full text in Research Archive
• Sanfilippo, Filippo (2024). Intelligent transportation system: Human Trajectory Simulation in Industrial Settings Using the Ornstein-Uhlenbeck Process and Deep Learning Based Classification. Full text in Research Archive
• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Das, Opy & Sanfilippo, Filippo (2024). Human-Robot Teaming for Intelligent Transportation Systems. Full text in Research Archive
• Sanfilippo, Filippo (2024). Exploring human-robot interaction, collaboration, and teaming. Full text in Research Archive
• Sanfilippo, Filippo (2024). IEEE Robotics and Automation, Control Systems and Intelligent Transportation Systems Joint Chapter, Norway. Full text in Research Archive
• Sanfilippo, Filippo (2024). Vision for IEEE Vice Chair of Member Activities. Full text in Research Archive
• Sanfilippo, Filippo (2024). Assessment and Coordination of the project Beyond the Classroom: Virtual Reality, Augmented Reality and Haptics for Enhanced Surgical Training and Education (ImmersiveSurgicalEdu). Full text in Research Archive
• Sanfilippo, Filippo (2024). Virtual Reality Experience of the Viking Ship - A Journey Through Time! Full text in Research Archive
• Sanfilippo, Filippo (2024). Disseminating research activities at Arendalsuka. Full text in Research Archive
• Sanfilippo, Filippo (2024). EMERALD project closing presentation. Full text in Research Archive
• Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2024). How to design, build, and test a haptic-enabled immersive tool for surgical education. Full text in Research Archive
• Sanfilippo, Filippo & Zafar, Muhammad Hamza (2024). Representing the Agder region at the AI, Data, Robotics Forum: live demos, posters and presentations. Full text in Research Archive
• Sanfilippo, Filippo (2024). Human-Robot Teaming, a forward leap into real life applications. Full text in Research Archive
• Sanfilippo, Filippo (2024). AI and Robotics-Enabled Systems. Full text in Research Archive
• Sanfilippo, Filippo (2024). IEEE: A Springboard for Young Professionals. IEEE Transmitter. Full text in Research Archive
• Sanfilippo, Filippo (2024). Assessment and Coordination of the project Beyond the Classroom: Virtual Reality, Augmented Reality and Haptics for Enhanced Surgical Training and Education (ImmersiveSurgicalEdu). Full text in Research Archive
• Sanfilippo, Filippo (2024). Snake Cobots: The Ultimate Companions for Human-Robot Teaming in Emergency Situations. IEEE Transmitter. Full text in Research Archive
• Sanfilippo, Filippo (2024). Lietuviai tyrėjai kartu su užsienio partneriais siekia pritaikyti virtualią realybę chirurgijos įgūdžiams tobulinti. [TV]. https://www.lrt.lt/naujienos/mokslas-ir-it/11/2233137/lietuv. Full text in Research Archive
• Sanfilippo, Filippo (2024). Modular Robotic Hands for Human-Robot Teaming (HRT). Full text in Research Archive
• Sanfilippo, Filippo (2024). Presenting research activities to Arendal Forskningsdagene. Full text in Research Archive
• Sanfilippo, Filippo (2024). Robotics used in Industry 4.0. Full text in Research Archive
• Gresli, Martin Bjørklund & Sanfilippo, Filippo (2024). Machine learning and IMU: Smart Gait applications for use in real-time systems. Universitetet i Agder. Full text in Research Archive Show summary

  Inertial Measurement Units hold a great potential for real-time gait phase measurements, gait kinematics measurement, and gait analysis. They offer convenience in the form of their light weight and portability. There are however known drawbacks in the form of sensor drift and susceptibility to noise. Extrapolating data for gait analysis directly from IMU’s can therefore be problematic, but emerging Machine learning / Artificial intelligence (ML/AI) modalities holds the potential of estimating gait characteristics to a new level of accuracy. Applications such as interpolation of user intent in prosthetic device control could through this be improved further, as well as movement analysis, and gait identification (GI) applications. In this thesis, IMU sensor data is used in combination with machine learning (ML) models to extrapolate significant information about the users walking gait. The information is extrapolated implicitly, through Machine Learning algorithms. The is centered around two applications: Gait detection (GD, i.e Gait phase detection), and Gait Identification (GI, i.e identify human subjects based on their walking gait). Both applications is already present in the scientific literature. The computational load for some of the systems is however quite high, and not fit for a live system running on a microcontroller or a mobile device. The proposed solution attempts to eliminate some of the computational load by reducing the dimension of input data, and reducing ML model complexity. The work yields two implementations of Machine Learning on a Gait Identity task, and includes discussion and analysis of important features for a ML Gait detection task for a dataset consisting of 18 features. For the first Gait Identity task, a TCN ML model architecture scored a perfect 1.0 on a Max F1-Averaged Stratified k-Fold validation for k=5 on a self-acquired single-IMU dataset with 13 subjects. On the second Gait Identity task, a TCN ML model architecture scored a 0.98 on a Max F1-Averaged Stratified k-Fold validation for k=5 on a publicly available single-IMU dataset with 30 subject

• Chen, BiHui; Tørresen, Jim & Sanfilippo, Filippo (2024). Simulation-to-real-world reinforcement learning with the PAL TIAGo robot. Universitetet i Oslo. Full text in Research Archive Show summary

  Reinforcement learning(RL) has been popular in the robotics field due to its ability to adapt to complex, dynamic situations. Conducting RL in the robotics field faces several challenges, such as sample efficiency problems, where the RL algorithm often requires a large amount of data sample to train RL agents to learn a desired behavior. This thesis proposes an RL framework that leverages the use of simulated data by training RL agents in the simulated environment. The model-free reinforcement learning algorithm and Proximal policy optimization algorithm were used in this thesis. Domain randomization techniques were applied to develop multiple simulated environments where different parameters were chosen and randomized. Subsequently, we evaluated the performance of each agent from these training environments on the physical TIAGo robot and compared their difference in performance. Furthermore, we used the Gazebo simulator to integrate with ROS to simulate the training environment. We also provide a dense reward design that guides agents' training to learn a desired behavior, which is described as learning to "reach," "aim," and "hold the gripper parallel" to reach a desired pre-grasp pose. The test conducted on the physical TIAGo robot shows that the learned pre-grasp pose can be utilized to successfully grasp a cube. The best performance agent, trained in the fully randomized environment, achieved an average success of 44% across five different tested cube placements when transferred to the physical TIAGo robot. In comparison, the agent trained in the fixed environment setup achieved an average success of 6% when transferred to the physical TIAGo robot. The results suggest that training in a higher variability based on the training environment setup used in this thesis improves transferability when transferring the agent from the simulation to the real world in terms of a higher empirical success rate and more consistent performance.

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